{"id":"W6920616493","doi":"10.6068/dp14ba8bb354b52","title":"Trend 1974 - 2006. Statistics Canada. CANSIM: Income, Pensions, Spending and Wealth - Pensions Plans and Funds and Other Retirement Income Programs | Country: Canada | Table: Registered pension plans (RPPs) and members, by class of employees eligible for the plan, sector, type of plan and contributory status | Variable: All employees, Plans, Defined benefit registered pension plans | Units: # %, 1974-2006. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-122.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pension; Descriptive statistics; Census; Social security; Socioeconomic status; Population; Publication; Official statistics; Personal income; Summary statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.001945213,0.001721321,0.002834294,0.0003918276,0.0005965469,0.0004535442,0.001366095,0.0009357518,0.0002287408],"category_scores_gemma":[0.000389327,0.001448523,0.000001392117,0.0005082694,0.001116362,0.0003842618,0.001446252,0.001162215,0.000002927691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004287405,"about_ca_system_score_gemma":0.005359618,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9968462,"about_ca_topic_score_gemma":0.9989093,"domain_scores_codex":[0.9906629,0.0007734537,0.002381039,0.002477403,0.00190889,0.001796323],"domain_scores_gemma":[0.9886742,0.003545809,0.002627491,0.003440494,0.0002777863,0.001434202],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002703995,0.0002197672,0.007911672,0.002502868,0.0009525996,0.0002672873,0.00004378826,0.00001090256,0.00008075959,0.0009014799,0.984348,0.00005688615],"study_design_scores_gemma":[0.006539615,0.001104117,0.0003355091,0.0008036843,0.001721078,0.0006503275,0.0007812302,0.00167675,4.951628e-7,0.000002473424,0.9848391,0.001545557],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004907274,0.01053535,0.000006097359,0.00001703538,0.0005365141,0.003203518,0.9847797,0.0001264528,0.0003046573],"genre_scores_gemma":[0.001518363,0.007489323,0.0004120433,0.0002143903,0.0001308515,0.00005132665,0.9887727,0.0006075469,0.0008034389],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.007576162,"threshold_uncertainty_score":0.9995533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0717893355598911,"score_gpt":0.290275696359484,"score_spread":0.2184863607995929,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}